深層時空間特徴学習と省間相互支援協調に基づく新型電力系統の再エネ受容能力評価・計画手法
Evaluation and Planning Method for Renewable Energy Carrying Capacity of Novel Power Systems Based on Deep Spatiotemporal Feature Learning and Inter-Provincial Mutual Assistance Synergy (原題)
Sudan Lai, Guilian Wu, Shiyuan Ni, Lu Tang, Jinlin Liao
🤖 gxceed AI 要約
日本語
中国の省級電力系統を対象に、時空間グラフ畳み込みネットワーク（STGCN）を用いて再エネ受容能力を動的に評価する手法を提案。源・系統・負荷間の非線形結合を能動的に補償し、省間相互支援と組み合わせた二段階協調計画モデルを構築した。シミュレーションにより計算効率と精度の向上、および省間融通による資源配分最適化を実証した。
English
Proposes a deep spatiotemporal graph convolutional network (STGCN) to dynamically evaluate renewable energy hosting capacity in a Chinese provincial grid, actively compensating nonlinear source-grid-load coupling. A two-stage collaborative planning model combines these boundaries with inter-provincial mutual assistance. Simulations show improved computational efficiency and accuracy, with cross-regional mechanisms optimizing resource allocation.
Unofficial AI-generated summary based on the public title and abstract. Not an official translation.
📝 gxceed 編集解説 — Why this matters
日本のGX文脈において
日本では再エネ大量導入に伴う系統制約・出力制御が喫緊の課題であり、広域連系（広域機関）や省間融通に相当する協調計画の知見は、日本の系統増強・マスタープラン議論に示唆を与える。ただし中国特有の省間制度が前提。
In the global GX context
Grid hosting capacity and cross-regional balancing are central to global energy-transition planning as variable renewables scale. The deep-learning-driven capacity evaluation offers a data-driven complement to conventional planning tools, though the inter-provincial framework is China-specific.
👥 読者別の含意
🔬研究者:深層学習を電力系統の受容能力評価・拡張計画に応用する際のSTGCN設計と二段階最適化の実装例として参考になる。
🏢実務担当者:再エネ調達や系統接続を検討する電力・需要家にとって、広域融通が受容能力をどう拡張するかの定量的示唆を得られる。
🏛政策担当者:再エネ大量導入下での系統増強・広域連系政策の設計において、データ駆動型の受容能力評価手法が政策立案の根拠となり得る。
📄 Abstract（原文）
INTRODUCTION: With the in-depth advancement of novel power system construction, the randomness and volatility brought by high-proportion renewable energy grid connections pose severe challenges to long-term planning. Traditional methods struggle with extracting complex non-linear features from massive data and face limited flexible resources within a single region. OBJECTIVES: This paper aims to propose a dynamic evaluation and collaborative planning method driven by deep spatiotemporal feature learning and inter-provincial power mutual assistance to accurately assess and expand the overall accommodation space. METHODS: Techniques derived from deep learning and graph neural networks are employed to conduct this research. Based on multi-source heterogeneous power grid operation measurement data, a spatial-temporal graph convolutional network (STGCN) model is constructed. Unlike traditional noise reduction methods, this model actively observes and compensates for the complex nonlinear coupling terms among source-grid-load. Subsequently, a two-stage collaborative planning model is established, utilizing the dynamic carrying boundaries output by the STGCN for grid expansion and operation simulation. RESULTS: The main results obtained in this paper are the following. Simulations on a certain provincial grid in China demonstrate that the STGCN model significantly improves computational efficiency and accuracy in extracting spatial-temporal features. Furthermore, the inter-provincial mutual assistance mechanism effectively optimizes resource allocation under the boundaries provided by the deep learning model. CONCLUSION: These are the conclusions of this paper: The integration of deep spatiotemporal learning with cross-regional mechanisms provides a highly accurate, data-driven computational framework for renewable energy carrying capacity evaluation and grid planning.
🔗 Provenance — このレコードを発見したソース
- primary_source https://doi.org/10.4108/ew.15077first seen 2026-10-24 17:23:48
🔔 こうした論文の新着を逃したくない方は キーワードアラート に登録（無料・3キーワードまで）。
gxceed は公開メタデータに基づく研究支援データセットです。要約・翻訳・解説は AI 支援で生成されています。 最終的な解釈・検証は利用者が原典資料に基づいて行うことを前提とします。